Inhaltsangabe:
Modern Inference Based on Health Related Markers: Biomarkers and Statistical Decision Making provides a compendium of biomarkers based methodologies for respective health related fields and health related marker-specific biostatistical techniques. The book introduces correct and efficient testing mechanisms, including procedures based on bootstrap and permutation methods with the aim of making these techniques assessable to practical researchers. In the biostatistical aspect, it describes how to correctly state testing problems, but it also includes novel results, which have appeared in current statistical publications. In addition, the book discusses also modern applied statistical developments that consider data-driven techniques, including empirical likelihood methods and other simple and efficient methods to derive statistical tools for use in health related studies.
Über die Autorinnen und Autoren:
Dr. Albert Vexler’s PhD degree in Statistics and Probability Theory was obtained from the Hebrew University of Jerusalem in 2003. His PhD advisor was Marcy Bogen, Professor, Fellow of the American Statistical Association. Dr. Vexler was a postdoctoral research fellow in the Biometry and Mathematical Statistics Branch at the National Institute of Child Health and Human Development. Currently, Dr. Vexler is Professor at the State University of New York at Buffalo, Department of Biostatistics. Dr. Vexler has authored and co-authored various publications that contribute to both the theoretical and applied aspects of statistics. His papers and statistical software developments have appeared in statistical and biostatistical journals, which have the top rated impact factors and are historically recognized as the leading scientific journals. Dr. Vexler was awarded a National Institute of Health (NIH) grant to develop novel nonparametric data analysis and statistical methodology. The results of this effort can be found via a public access resource housed by the US National Library of Medicine.
Dr. Albert Vexler has belonged to the first cohort of investigators that proposed and discovered novel density-based empirical likelihood methodology. He has introduced the density-based empirical likelihood approach for creating nonparametric test statistics that efficiently approximate optimal parametric Neyman-Pearson statistics using minimum distribution assumptions on data. Recently, several statistical academic books referred the density-based empirical likelihood methodology to classical statistical procedures.
Dr. Jihnhee Yu obtained her PhD in Statistics from Texas A&M University in 2003. Currently Dr. Yu is Associate Professor at the State University of New York at Buffalo, Department of Biostatistics, and Director, Population Health and Health Observatory, School of Public Health and Health Profession, SUNY at Buffalo. She has authored and coauthored scientific papers published in several peer-reviewed journals throughout her career. Dr. Yu main research interests are clinical trials designs, parametric and nonparametric likehood approach.
SUNY Distinguished Professor, Dept. of Biostatistics, SPHHP, Assistant Director, IHI at University at Buffalo, Adjunct Professor, Computer Science
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